{"repo":"Cerno-AI/Cerno-Agentic-Local-Deep-Research","free":true,"listed":false,"github":"https://github.com/Cerno-AI/Cerno-Agentic-Local-Deep-Research","clone":"git clone https://github.com/Cerno-AI/Cerno-Agentic-Local-Deep-Research.git","description":"Cerno is a local-first research platform that leverages agentic AI to break down complex queries into verifiable, multi-step workflows. Switch seamlessly between cloud LLMs and self-hosted models, track every reasoning step, and optimize cost and tokens—all while keeping your data on your machine.","language":"Python","stars":84,"topics":["agentic-ai","agents","anthropic","autonomous-agents","claude","data-sovereignty","deep-research","deep-research-agent","deepresearch","deepseek"],"license":"MIT","category":"ai-agents","readme_excerpt":"Cerno : Agentic Deep Research Cerno is an open-source workspace for conducting deep , multi-step research and analysis using autonomous AI agents. Designed for developers and researchers who demand analytical transparency , Cerno exposes every reasoning step—from prompt decomposition to final synthesis—so you can observe, debug, and steer complex agentic workflows with confidence. 📚 Table of Contents 1. Highlights 2. Local-First Principles 3. Active Development & Community 4. Prerequisites 5. Docker Installation 6. CLI Reference 7. Project Structure 8. Screenshots 9. Use Cases 10. Roadmap 11. Security & Privacy 12. Contributing 13. License --- 🚀 Highlights Model-Agnostic Core : Effortlessly switch between premier LLMs (OpenAI, Google Gemini, Anthropic, DeepSeek) or run local models via Ollama. Zero-Config Setup : One CLI, one command—automatically create a virtual environment, install dependencies, and configure your workspace. Transparent Execution Plan : Visualize each agent task as it moves through Pending → Running → Success/Error states in real time. Verifiable Artifacts : Every source, webpage, and generated file (reports, code, data) is tracked and organized for easy auditing. Adaptive Depth : Simple queries spawn lightweight plans; complex directives trigger multi-agent, multi-tool orchestrations. Token & Cost Optimization : A manager-worker agent architecture balances quality and cost. Get a complete cost breakdown upon task completion. Local-First Ethos : Work off","default_branch":null,"files":null,"tree":[],"storefront":"/r/Cerno-AI","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Cerno-AI/Cerno-Agentic-Local-Deep-Research/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}